Abstract
Question Answering (QA) has become a popular topic of research in the Natural Language Processing (NLP) community in recent years. This means that researchers and enthusiasts in the field of NLP have been actively working on developing models and improving existing ones to better answer questions. However, there are fewer studies on Arabic QA compared to other languages, and even fewer on QA for the Quran. BERT is a deep neural network model that has outperformed other models on the SQuAD benchmark. BERT is known for its ability to understand contextual information and provide accurate answers. Therefore, it is a promising model for Quranic QA. In this paper, we will abord to a comparative study of different models based on BERT and used by researchers in the religious field of MRC more precisely the Holy Quran.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hamdelsayed, M.A., et al.: Islamic application of question answering systems: comparative study. J. Adv. Comput. Sci. Technol. Res. 7(1), 29–41 (2017)
Utomo, F.S., Suryana, N., Azmi, M.S.: Question answering system: a review on question analysis, document processing, and answer extraction techniques. J. Theor. Appl. Inf. Technol. 95(14), 3158–3174 (2017)
El Bazi, I., Laachfoubi, N.: Arabic named entity recognition using deep learning approach. Int. J. Electric. Comput. Eng. 9(3), 2088–8708 (2019)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis (2019)
Mozannar, H., Maamary, E., El Hajal, K., Hajj, H.: Neural Arabic question answering. In: Proceedings of the Fourth Arabic Natural Language Processing Workshop, pp. 108–118, Florence. Association for Computational Linguistics (2019)
Mozannar, H., Hajal, K.E., Maamary, E., Hajj, H.: Neural Arabic question answering. arXiv preprint (2019). arXiv:1906.05394
Utomo, F.S., Suryana, N., Asmi, M.S.: Question answering systems on holy quran: a review of existing frameworks, approaches, algorithms and research issues. J. Phys.: Conf. Ser. (2020)
Antoun, W., Baly, F., Hajj, H.: AraBERT: transformer-based model for Arabic language understanding. In: Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, Marseille, pp. 9–15. European Language Resource Association (2020)
Abdelali, A., Hassan, S., Mubarak, H., Darwish, K., Samih, Y.: Pre-training Bert on Arabic tweets: practical considerations (2021)
Abdul-Mageed, M., Elmadany, A., Nagoudi, E.M. B.: ARBERT & MARBERT: deep bidirectional transformers for Arabic. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7088–7105. Association for Computational Linguistics (2021)
Mohammed, E., Amany, M.S.: Computation and Language TCE at Qur’an QA 2022: Arabic Language Question Answering Over Holy Qur’an Using a Post-Processed Ensemble of BERT-Based Models (2022)
Rana, M., Tamer, E.: Arabic machine reading comprehension on the Holy Qur’an using CL- AraBERT. Inf. Process. Manag. (2022)
Esha, A. Muhammad, K.M.: eRock at Qur’an QA 2022: contemporary deep neural networks for Qur’an based reading comprehension question answers. In: Proceedings of the OSACT 2022 Workshop @LREC2022, Marseille, pp. 96–103. European Language Resources Association (ELRA) (2022)
Malhas, R., Mansour, W., Elsayed, T.: Qur’an QA 2022: overview of the first shared task on question answering over the holy Quran. In: Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5) at the 13th Language Resources and Evaluation Conference (LREC 2022) (2022)
Ahmed, W., Eman, E., Marwa, M., Haq, N.: Stars at Qur’an QA 2022: building automatic extractive question answering systems for the holy Qur’an with transformer models and releasing a new dataset. In: Proceedings of the OSACT 2022 Workshop @LREC2022, Marseille, pp. 146–153 (2022)
Abdullah, A., et al.: LK2022 at Qur’an QA 2022: simple transformers model for finding answers to questions from Qur’an. In: Proceedings of the OSACT 2022 Workshop @LREC2022, Marseille, pp. 120–125 (2022)
Aly, M., Omar, M.: GOF at Qur’an QA 2022: towards an efficient question answering for the holy Qu’ran. In: The Arabic Language Using Deep Learning-Based Approach. In Proceedings of the OSACT 2022 Workshop @LREC2022, Marseille, pp. 104–111 (2022)
Alwaneen, T.H., Azmi, A.M., Aboalsamh, H.A., Cambria, E., Hussain, A.: Arabic question answering system: a survey. Artif. Intell. Rev. 55(1), 207–253 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Reggad, S., Ghadi, A., El Aachak, L., Samih, A. (2024). Machine Reading Comprehension for the Holy Quran: A Comparative Study. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., KaraÈ™, Ä°.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-54376-0_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54375-3
Online ISBN: 978-3-031-54376-0
eBook Packages: EngineeringEngineering (R0)